code: def trainer(
      experiment_path: str, run_config: train.RunConfig
  ) -> train.Result:
      dataset_size = 128
      num_workers = 4
  
      def train_loop_per_worker(config):
          # Wrap the other train_fn with a check for the dataset.
          assert train.get_dataset_shard("train")
          train_fn(config)
  
      datasets = {
          "train": ray.data.range(dataset_size),
          "valid": ray.data.read_csv(CSV_DATA_FILE),
      }
  
      if DataParallelTrainer.can_restore(experiment_path):
          trainer = DataParallelTrainer.restore(
              experiment_path,
              datasets=datasets,
              train_loop_per_worker=train_loop_per_worker,
          )
      else:
          trainer = DataParallelTrainer(
              train_loop_per_worker,
              datasets=datasets,
              scaling_config=train.ScalingConfig(
                  num_workers=num_workers, trainer_resources={"CPU": 0}
              ),
              run_config=run_config,
          )
  
      result = trainer.fit()
      return result
  
imports: import collections
  import json
  import os
  from pathlib import Path
  import random
  import time
  from typing import Dict, List, Optional
  import ray
  from ray import train, tune
  from ray.train.data_parallel_trainer import DataParallelTrainer
  from ray.tune.experiment import Trial
  from ray.train.tests.util import create_dict_checkpoint, load_dict_checkpoint
label_desc: Use image node_id 3 for calling a global function with example usage: trainer(experiment_path, run_config) and returns: result